Over the past four years, it has been extremely challenging for executives to forecast demand for packaging. COVID-19, supply chain shocks, raw material fluctuations and stocking/destocking dynamics have all made it difficult to have confidence in short-term (60-90 days) and medium-term (one to two years) demand forecasting and associated financial, operational budgeting and capital planning. And given executives’ interest in greater fidelity in customer segmentation, advanced demand forecasting approaches have emerged as a critical tool for success.
Leveraging cutting-edge machine learning (ML) and artificial intelligence (AI) technologies, advanced forecasting surpasses traditional demand budgeting methods, delivering heightened precision, agility and insight. For industry participants across the value chain, particularly converters and distributors, that means reduced costs, optimized operations, and an enhanced ability to identify and capture market opportunities. In this edition of L.E.K. Consulting’s Executive Insights, we explore how advanced demand forecasting powered by AI addresses key challenges and drives transformative value for packaging value chain participants.
The evolution of demand forecasting in packaging
Traditional limitations
Traditional demand forecasting methods often rely heavily on historical data, trend analysis and customer-provided forecasts. While these approaches have long served as a baseline for planning, they present several shortcomings and struggle to keep pace with the dynamic nature of today’s markets. First, customers often suffer from bias in their own forecasting process, which can be prone to idiosyncratic order patterns or major misalignments in near-term projections and order volumes.
Second, demand surges driven by shifting consumer preferences or economic disruptions are often overlooked or poorly anticipated, leading to costly mismatches between production and actual needs (or to the misallocation of sales resources). Furthermore, traditional models often fail to adjust effectively to seasonality and external macroeconomic factors, leaving packaging executives reactive rather than proactive.
The advanced approach
Modern demand forecasting leverages the power of ML algorithms to analyze complex and multidimensional datasets. By integrating data sources such as point-of-sale consumer movements, longitudinal macroeconomic trends and supply chain disruptions, these advanced tools enable significantly higher levels of precision and adaptive forecasting. For instance, AI-driven models can identify emerging demand trends early, allowing packaging executives to adjust production schedules, align resources and stay ahead of competitors in an industry where agility and precision are paramount.
Key challenges for packaging executives — and how advanced forecasting solves them
Inventory management
Inventory mismanagement — whether excess stock or shortages — is a persistent issue for packaging executives. Excess inventory ties up working capital and increases carrying costs, while shortages lead to stockouts, frustrated customers and lost revenue. Advanced demand forecasting helps align production with actual demand, minimizing these risks. Additionally, reducing lead times through better forecasting enables packaging industry participants to differentiate themselves in a competitive market, enhancing customer satisfaction and retention. Industry participants can better align purchasing to orders and thereby minimize working capital needs.
Operational efficiency
Packaging executives typically must manage high fixed costs and the ongoing challenge of maximizing plant utilization. Traditional forecasting methods often leave gaps in demand predictions, leading to suboptimal scheduling and resource allocation. With ML-enabled forecasting, executives can better predict demand peaks and troughs with precision, which allows for optimized production schedules, better labor allocation and improved machine utilization, driving significant efficiency gains and cost savings.
Capturing growth opportunities
Constantly changing consumer preferences means a growing interest in increasingly subsegmented customers and ever-evolving opportunities for packaging executives. Identifying which market subsegments and packaging formats are poised for growth requires nuanced insight. Advanced forecasting enables executives to track emerging demand patterns (e.g., organic), pinpoint specific high-growth subcategories (e.g., organic baby food), identify “winning” brands and formats (e.g., organic baby food in compostable pouches) and proactively adjust product offerings. As a result, executives are empowered to capture market share in emerging niches and maintain relevance as consumer preferences evolve.
Navigating supply chain disruptions
Supply chain volatility, including material shortages and logistics delays, poses significant challenges for packaging executives. Traditional forecasting methods often fail to incorporate external supply chain factors, leaving businesses vulnerable. Advanced forecasting, by contrast, integrates the latest supply chain data to dynamically adjust demand predictions.
This proactive approach helps packaging players mitigate risks, maintain production schedules and meet customer demands despite disruptions (see Figure 1).
